International Journal of Science, Engineering and TechnologyAuthors: Yeola Tanishka Manoj, Prof. S. S. Bhor, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Crop diseases remain a serious threat to food security, causing substantial yield losses and economic hardship for farmers. Traditional detection depends on manual field inspection by experts, which is slow, subjective and often unavailable in rural regions. This paper systematically reviews AI-based approaches for crop disease prediction. It covers classical machine learning algorithms such as support vector machines, random forests and k-nearest neighbours applied to colour, texture and shape features, and deep learning architectures including convolutional neural networks, transfer-learning models and vision transformers. The complete pipeline is discussed: image acquisition through smartphones, drones and IoT sensors; preprocessing and segmentation; feature extraction; classification; and deployment on mobile and edge platforms using lightweight architectures and quantization. Commonly used datasets including PlantVillage are examined together with evidence of their background bias and the resulting gap between laboratory and field accuracy, and field datasets such as PlantDoc and the Plant Pathology challenge. Evaluation metrics of accuracy, precision, recall and F1-score are reviewed, as is the integration of environmental parameters such as temperature, humidity and soil conditions with image data for forecasting. A five-stage architecture is proposed, with research gaps, objectives, a comparison of techniques and challenges including limited real-field data, class imbalance, lighting variation, computational cost and poor generalization. Future directions identified are explainable AI, lightweight models, multimodal fusion and federated learning.
DOI: https://doi.org/10.5281/zenodo.24214661
